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Titlebook: Computer and Information Sciences; 31st International S Tadeusz Czachórski,Erol Gelenbe,Ricardo Lent Conference proceedings‘‘‘‘‘‘‘‘ 2016 Th

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An Adaptive Heuristic Approach for the Multiple Depot Automated Transit Network Problemice. Within this context of ATN, this study tries to deal with a specific routing problem arising in the context of a ATN’s network with a multiple depot topology. More specifically, we present an optimization routing model for automated transit networks which can be used to strategically evaluate d
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An Analysis of the Taguchi Method for Tuning a Memetic Algorithm with Reduced Computational Time Bud, it is often a time-consuming task and needs to be performed under a restricted computational budget. In this study, the results from our previous work on using the Taguchi method to tune the parameters of a memetic algorithm for cross-domain search are further analysed and extended. Although the T
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Ensemble Move Acceptance in Selection Hyper-heuristicsnce is a crucial component of selection hyper-heuristics, deciding whether to accept or reject a new solution at each step during the search process. This study investigates group decision making strategies as ensemble methods exploiting the strengths of multiple move acceptance methods for improved
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Extending Static Code Analysis with Application-Specific Rules by Analyzing Runtime Execution Tracescation of these rules. However, such rules are usually not documented explicitly. Moreover, the implementation of specialized checkers is a manual process that requires expertise. In this work, application-specific programming rules are automatically extracted from execution traces collected at runt
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The Random Neural Network Applied to an Intelligent Search Assistantre influenced by the users’ own ambiguity in formulating their requests or queries as well as by the commercial interest of Web search engines and Internet users that want to reach a wider audience. This paper presents an Intelligent Search Assistant (ISA) based on a Random Neural Network that acts
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A Novel Grouping Genetic Algorithm for the One-Dimensional Bin Packing Problem on GPUnumber of bins. Large problem instances of the 1D-BPP cannot be solved exactly due to the intractable nature of the problem. In this study, we propose an efficient Grouping Genetic Algorithm (GGA) by harnessing the power of the Graphics Processing Unit (GPU) using CUDA. The time consuming crossover
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